Automated lifetime-based screening and characterization  of fluorescent proteins

Automated lifetime-based screening and characterization of fluorescent proteins

Daphne Bindels, Dorus Gadella and Marten Postma, University of Amsterdam

Researchers at the University of Amsterdam developed a multi-position fluorescence lifetime imaging (FLIM) screening method to screen for bright FPs. However, this method can be applied to any experiment in which the fluorescence lifetime is an important parameter.

In biological research, fluorescent proteins (FPs) are widely used in fluorescence microscopy. FPs are genetically encoded fluorophores, which are produced by cells after a noninvasive transfection. These proteins are utilized as markers and bio-sensors to study biological processes in living cells at high spatial and temporal resolution. Currently, many different types of FPs exist, comprising different spectral properties (i.e. colors), fluorescence lifetime characteristics, photo switching behavior, brightness, pH sensitivity, etc.

New FPs are continuously being developed in order to optimize their properties for specific applications. The fluorescence lifetime is an excellent parameter to use as selection marker in a screening for optimized FPs. The fluorescence lifetime is correlated with the quantum yield and thus the intrinsic brightness. In contrast, the fluorescence intensity is correlated with the brightness and protein concentration. Therefore, when only fluorescence intensity is used for screening, the selection will be biased towards samples with a higher concentration. Also the effects of environment can affect the fluorescence lifetime, e.g. the pH and chloride ions. A main application where the fluorescence lifetime is a key molecular property, is in Förster Resonance Energy Transfer (FRET) based molecular biosensors. Here changes in molecular interactions or conformation can be directly observed by measuring the donor life-time and changes thereof.

We developed a multi-position fluorescence lifetime imaging (FLIM) screening method to screen for bright FPs. However, this method can be applied to any experiment in which the fluorescence lifetime is an important parameter. To be able to test many different FP mutants (samples), an efficient acquisition and data processing workflow is paramount to obtain high quality and reproducible data with high throughput. The LIFA system, with the aid of MATLAB® and ImageJ [1], can be utilized to set up such an automated screening and characterization approach, allowing full flexibility.

Automated multi-position FLIM acquisition with MATLAB

Lambert Instruments has provided a versatile library of interface functions (API) that allows direct communication with the LIFA software via MATLAB, in order to control the LIFA camera (Fig. 1A) and the microscope (Fig. 1B). With the aid of this API, a custom made MATLAB graphical user interface (GUI) was developed that allows multi-position acquisition.

Figure 1. Automated 96-well lifetime acquisition. The LIFA camera (A) and microscope (B) iterate over a 96 well plate (C). Each well can be selected or deselected by using the well selector (D). The selected coordinates are loaded into the MATLAB GUI and specific excitation and emission filters can be selected together with auto-exposure and output file settings (E). Finally, lifetime files (fli) are exported to a large TIF hyperstack (F) for further processing with ImageJ.

In this example, each well in a 96-well plate contains mammalian cells transfected with DNA encoding a different FP (Fig. 1C). Prior to the multi-position acquisition, specific wells can be selected or deselected by using a secondary MATLAB GUI (Fig. 1D). After this, a text file and an Excel file are exported, both only containing the selected wells. The Excel file comprises the well names, the well coordinates and also contains a column where the user can add metadata about each well (e.g. name of FP).

Before acquisition is started, it is possible to set other parameters within the LIFA software and to acquire a calibration lifetime measurement. Furthermore, options to use auto-exposure and to choose the type of output files that will be stored for later processing are present (i.e. calculated lifetime images, raw phase images or DC images) (Fig. 1E). After the coordinate file is loaded and acquisition is commenced, the MATLAB script iterates over all coordinates, taking phase stacks and storing the selected output files. Each lifetime file (.fli) comprises four channels, including lifetime data and the fluorescence intensity data. The files also contain metadata about the position, exposure time and reference lifetime. Using another secondary custom made MATLAB GUI, all the fli-files are imported using the Bio-Formats [2] package for MATLAB and the image data and metadata are extracted. Subsequently, all the raw images are exported to an ImageJ TIF hyperstack (Fig. 1F) that can be directly processed with ImageJ. The reference lifetime, the exposure time for each well as well as the information previously stored in the Excel file are added to the metadata for later use. This helps the user to keep track of the wells and their metadata during processing.

Data processing and visualization using ImageJ

In order to process the large quantities of lifetime data, several ImageJ macros were written that can be easily operated by any user. The user sets several parameters before processing, including smoothing and also thresholding (Fig. 2A). The macro generates three files: an ImageJ TIF stack (Fig. 2B), a 96-well layout figure (Fig. 2C), and a numerical table. The 96-well layout displays the mean phase lifetime, mean modulation lifetime and mean intensity of all wells and will therefore quickly indicate the wells of interest. Each multi-panel FLIM figure of the ImageJ TIF stack gives a detailed FLIM analysis per well. The multi-panel FLIM figure (Fig. 2D) displays the intensity image in grayscale (I) and the following panels for the phase and modulation lifetimes: a false color image (II), a false color image with intensity overlay (III), a lifetime histogram (IV), a scatterplot of the lifetime versus the intensity (V). Finally, the polar plot (VI) is shown together with the scatterplot of phase versus modulation lifetime (VII). In addition, each multi-panel FLIM figure also includes metadata i.e. the exposure time, well number, the user added metadata, and the original file name.

Figure 2. An ImageJ macro (A) allows to set several processing and display parameters and generates a TIF stack with display figures for all wells present in the hyperstack (B) and a 96-well layout with the mean lifetime data (C). A detailed FLIM analysis from one well (D), the TIF stack (B) containing an extensive FLIM analysis of each well. The multi-panel D contains: grayscale (I), lifetime false color images (II), lifetime false color image with intensity overlay (III), a lifetime histogram (IV), a scatterplot of the lifetime versus the intensity (V), the polar plot (VI) and a scatterplot of phase (phi) versus modulation (mod) lifetime (VII).

The wells in Figure 2 are measurements of newly developed mScarlet red FP variants [3] in mammalian cells. The measurements are highly reproducible; in figure 2 only 4 variants have been used and only 4 colors appear in the phase and modulation lay-out in figure 2C. The lifetime histograms (Fig. 2D IV) of different wells that are transfected with the same FP are very similar. The width of the histograms mainly reflects the variability across cells, and is much narrower for measurements performed on solutions with purified FPs. By using this approach we found a monomeric red FP variant, named mScarlet, with the highest fluorescence lifetime and quantum yield up to date. As mentioned before, this pipeline can also be utilized for other screening and characterization applications where fluorescence lifetime is an important readout, for example optimization of FRET based biosensors, testing of agonist and antagonist dose dependency and pH sensitivity of fluorescent proteins. Taken together, using this pipeline, it is possible to screen many constructs and conditions in a fast automated and user-friendly way, yielding robust and highly reproducible results.


Lambert Instruments (Johan Herz MATLAB API scripting), NWO-STW grant 12149, NWO CW-Echo grant 711.01.01812, NWO ALW-VIDI grant 864.09.015 and Nikon Netherlands


1. Schneider, C. A.; Rasband, W. S. & Eliceiri, K. W. (2012), NIH Image to ImageJ: 25 years of image analysis, Nature methods 9(7): 671-675.

2. Melissa Linkert, Curtis T. Rueden, Chris Allan, Jean-Marie Burel, Will Moore, Andrew Patterson, Brian Loranger, Josh Moore, Carlos Neves, Donald MacDonald, Aleksandra Tarkowska, Caitlin Sticco, Emma Hill, Mike Rossner, Kevin W. Eliceiri, and Jason R. Swedlow (2010) Metadata matters: access to image data in the real world. The Journal of Cell Biology, Vol. 189no. 5777-782.

3. Daphne S. Bindels, Lindsay Haarbosch, Laura van Weeren, Marten Postma, Katrin E Wiese, Marieke Mastop, Sylvain Aumonier, Guillaume Gotthard, Antoine Royant, Mark A. Hink and Theodorus W.J. Gadella Jr. mScarlet: a novel bright monomeric red fluorescent protein for cellular imaging. Nature Methods (2016)

Fluorescence Lifetime Imaging with a Time-Domain FLIM System on a Widefield Microscope

Fluorescence lifetime can be recorded for every pixel in the image simultaneously with a time-domain FLIM camera. This method requires an intensified camera, a pulsed laser and a widefield fluorescence microscope. This is typically more cost-effective than alternative methods that need a confocal set-up.

One of the most popular methods for fluorescence lifetime imaging microscopy (FLIM) is time-correlated single photon counting (TCSPC). This method requires a confocal microscope with a pulsed laser and a photomultiplier tube (PMT). The sample is briefly illuminated by a laser pulse after which the PMT counts the number of emitted fluorescence photons. The intensity I of the fluorescence emission decays exponentially after the laser pulse has excited
the sample:

    I(t) = I_0\exp\left(-\frac{t}{\tau}\right).

The fluorescence lifetime \(\tau\) quantifies the rate of decay of the fluorescence light. By scanning the sample with a focused laser beam, TCSPC systems can construct a fluorescence lifetime image of the sample one pixel at a time.

As an alternative to TCSPC on a confocal microscope, Lambert Instruments has developed a new system that brings time-domain FLIM to widefield microscopes. By carefully timing the exposure of the camera in the subnanosecond range, a light pulse profile of the fluorescence light can be captured. This method requires a pulsed laser and an intensified camera to record the raw data. Custom Lambert Instruments software then processes this data to automatically calculate the fluorescence lifetime.


Images were recorded with the LIFA-TD, which has a CCD camera with a fiber-optically coupled image intensifier. The image intensifier boosts the incoming light levels and it can achieve gate widths of less than 3 ns. A 485 nm pulsed laser (Picoquant LDH-D-C-485 laser head with a PDL 800-B laser driver) with a fiber-optical output was coupled into a widefield fluorescence microscope (Nikon Eclipse Ti) to provide 85 ps excitation pulses.


The set-up was calibrated by recording the light pulse profile of the laser by placing a highly reflective material in the sample holder of the microscope. Next, the fluorescence decay profile of a convallaria (lily of the valley) sample was recorded. The fluorescence lifetime is determined by correlating the fluorescence emission to the light pulse profile.


Figure 1 shows the fluorescence lifetime of a convallaria sample overlayed on the original image. The LIFA-TD is able to detect the small variations in fluorescence lifetime between different parts of the sample, stained with different dyes.

Figure 1: Fluorescence intensity (left) recording of convallaria sample and corresponding fluorescence lifetimes (right) overlayed on the original image.

Figure 1: Fluorescence intensity (left) recording of convallaria sample and corresponding fluorescence lifetimes (right) overlayed on the original image.


Time-domain fluorescence lifetime imaging microscopy can be done on a widefield fluorescence microscope by using an intensified camera and a pulsed laser. The LIFA-TD is an entry-level FLIM system that offers an integrated solution.

Revealing Cancer's Infrastructure

This year marks the 10th anniversary of the LIFA. With the first Lambert Instruments FLIM Attachment (LIFA) a decade ago, we introduced an easy and fast approach to fluorescence lifetime imaging. Since then, we advanced our imaging and analysis software; we improved our hardware and made it more compact; and we added compatibility with third-party hardware. But at the heart of the LIFA experience are still the features that matter most to our users. They are using the LIFA every day, because it is the easiest and fastest system for fluorescence lifetime imaging microscopy.

We visited Dr. Kees Jalink of the Biophysics of Cell Signaling group at the Netherlands Cancer Institute. His research group purchased the first ever LIFA to leave the labs of Lambert Instruments. Ten years later, the LIFA is still their fluorescence lifetime imaging method of choice for studying signal transduction pathways in living cells.

"Cancer can only be truely understood by knowing it in great detail," says Jalink, "that is, by knowing exactly where and when signal transduction pathways become activated. Tools to study signals must yield data with spatial and temporal detail from living cells, preferably from cells that are as much as possible in a natural environment."

"We use FRET sensors to study living cells. So for us, some of the other fluorescence lifetime imaging methods are just unacceptably slow. Our researchers are always in a hurry, because the cells they want to study expire within a couple of hours. The LIFA allows us to quickly record fluorescence lifetime images and immediately see a visualization of the results. It is the only way to quickly gather quantitative FLIM data."

Top row: Light intensity images (colorized). Bottom row: Corresponding fluorescence lifetime images (colorized). The average fluorescence lifetime of the cells increases over time, as shown in the graph on the right.

Top row: Light intensity images (colorized). Bottom row: Corresponding fluorescence lifetime images (colorized). The average fluorescence lifetime of the cells increases over time, as shown in the graph on the right.

All researchers and students in Jalink's group are trained to work with the LIFA to analyze their cells. "After an introduction of about 30 minutes, everybody can work with the system. It's a simple procedure and that's why we use the LIFA nearly every day."

More Information

Lambert Instruments has been shipping the LIFA to cancer research facilities all over the world for years. We are hopeful that we have facilitated a major advance in cancer research. If you are in this field of research, please contact us if you have any questions about the LIFA.


Over the years, many researchers have published the results of experiments they performed with the LIFA. We have compiled an overview of LIFA publications, as well as a number of application notes that illustrate some of its many applications.

High-speed in vivo imaging of a zebrafish heart

Recording images of living organisms at high frame rates with a fluorescence microscope is challenging. High-speed imaging requires a considerable light intensity, because at high frame rates the image sensor is exposed to light very briefly. During that short period of time, enough light needs to be captured to obtain a clear image. Normally, this is achieved by increasing the intensity of the illumination. Because the more light bounces off the object, the more light reaches the camera. But when studying fluorescence or chemiluminescence, the object itself emits light and increasing the intensity of the emitted light is often not possible. In such a situation, the solution is to increase the intensity of the light that is detected by the camera.

Imaging the cardiovascular system of a zebrafish

At the Max Planck Institute for Heart and Lung Research in Bad Nauheim (Germany), the cardiovascular system of the zebrafish is studied. The transparency of the zebrafish (figure 1) and its experimental advantages make it an ideal scale model of the human cardiovascular system.

Figure 1. Photo of a zebrafish. The heart is located inside the red square.

Figure 1. Photo of a zebrafish. The heart is located inside the red square.

To study the blood flow in a zebrafish, the red blood cells are labeled with the fluorescent protein DsRed. The intensity of the fluorescent light is limited by the finite number of fluorescent proteins attached to the red blood cells. Also, the direction in which the light is emitted is random, which further decreases the amount of light reaching the camera. A low light intensity is not necessarily problematic. Increasing the exposure time to capture sufficient light is a well known method for imaging dim objects that are stationary. However, using the same method on a moving object results in blurred images.

When imaging a living zebrafish, its internals are moving. The heart rate of a zebrafish is approximately 175 bpm, or nearly 3 beats per second. To capture each phase of the heart beat requires a high frame rate, because otherwise the images will be distorted by motion blur. This means the image sensor is being exposed to the dim fluorescent light very briefly. Increasing the amount of fluorescent light by increasing the intensity of the excitation light is not an option, as this would harm the fish. 

Figure 2. HiCAM attached to fluorescence microscope.

Figure 2. HiCAM attached to fluorescence microscope.


Experimental setup

The zebrafish is studied with a fluorescence microscope with high-speed camera system mounted to it (figure 2). The fish is fixated in a gel and illuminated from below. Fluorescent light from the DsRed protein is emitted from the red blood cells. This light is emitted in every direction, some of it traversing the optical path of the laser in the opposite direction. But instead of being reflected back towards the light source, the fluorescent light is directed towards a camera through a dichroic mirror. Any scattered excitation light is reflected by the dichroic mirror. An optical filter removes any background light and only transmits light at the wavelength emitted by fluorescence of the red blood cells.

An image sensor will capture the incoming fluorescent light. At frame rates of hundreds or thousands of frames per second, the exposure time for each frame is in the order of several milliseconds to fractions of milliseconds. Electron-Multiplied CCD (EMCCD) sensors have a light sensitivity that is good enough to capture the dim fluorescent light. But they can only achieve frame rates up to approximately 100 fps at full resolution, which is not enough for the application at hand. CMOS sensors can operate at higher frame rates, up to thousands of frames per second at full resolution. However, during the short exposure time of each frame, a regular high-speed CMOS sensor is not able to record a sufficient amount of light to achieve a reasonable signal-to-noise ratio. 


Figure 3. Red blood cells in the heart of a zebrafish (bottom right corner of images) are transported from one chamber to the next chamber (a-c) and into the aorta (e). Images shown here were recorded with an interval of 25 ms between them. Recording was done at 2000 fps with an exposure time of 500 us.


Advantages of the HiCAM

The HiCAM achieves both the required light sensitivity and the high frame rates by combining a high-speed CMOS sensor with an image intensifier. The image intensifier increases the number of detected photons by several orders of magnitude. This way, it is possible to record the blood flow in a zebrafish at 2000 frames per second. Figure 3 shows the flow of red blood cells through the cardiovascular system of the zebrafish.

Time-resolved fluorescence in the analysis of edible oils

Fluorescence spectroscopy is an effective method to obtain a physical or chemical signature for delineating the composition and characteristics of organic matter. It is a major tool for analyzing food security. However, since most organic ingredients have similar fluorescent spectra, it is difficult to distinguish them with high precision by traditional fluorescence analysis.

PhD Mu, considering the time characters of the fluorescent spectra, develops a new method based on time-resolved fluorescence. The time resolution is 3 ns realized by a TRiCAM: a gated, intensified CCD camera by Lambert Instruments. The contour diagrams of the time-resolved fluorescence intensities (CDTRFIs) of different kinds of edible oils are acquired. Outperforming traditional fluorescence analysis, CDTRFIs greatly improve the identification capabilities without sacrificing the advantages of traditional fluorescence analysis.

Figure 1. Different shapes of the LIF spectra of the rapeseed oil.

Figure 1. Different shapes of the LIF spectra of the rapeseed oil.

The shapes of the laser-induced fluorescence (LIF) spectra  of the edible oils change over time. Figure 1 shows an example of such a variation in the fluorescence spectral shapes of rapeseed oil. In this example, the gate width (GW) is fixed at 3 ns, and the time gates (TGs) were at 3, 13, 23, 33, and 43 ns. Given the various fluorescence lifetimes of the excited aromatic compounds, the fluorescence spectrum of edible oils varies at different time windows. This variation explains why the shapes of the fluorescence spectrum vary over time. These changes in the spectra vary depending on the oil type and can be conveniently used to distinguish different oil types. Contour diagrams using fluorescence wavelength and TGs as axes are used to show the changes in LIF shapes over time.

Figure 2 shows contour diagrams constructed this way when all the spectra are normalized and only one excitation wavelength (355 nm) is used [1]. The vertical axis of the CDTRFIs has wavelengths ranging from 390 nm to 720 nm.  In addition, the time range along the horizontal axis is 45 ns, with a 3 ns sampling interval. Then fluorescence should be measured at 15 different TGs for each oil to construct an intact CDTRFI.

Figure 2. Contour diagrams of normalized time-resolved fluorescence intensities  of (a) olive, (b) rapeseed, (c) grapeseed, (d) soybean, (e) corn, (f) peanut 1, (g) peanut 2, and (h) peanut 3 oils. The fluorescence wavelength (y-axis) and detection time (x-axis) are used as axes. The wavelength is 390 nm to 720 nm, and the time range is 45 ns with an excitation wavelength of 355 nm. The gate width of the TRiCAM  is set to 3 ns.

Figure 2. Contour diagrams of normalized time-resolved fluorescence intensities  of (a) olive, (b) rapeseed, (c) grapeseed, (d) soybean, (e) corn, (f) peanut 1, (g) peanut 2, and (h) peanut 3 oils. The fluorescence wavelength (y-axis) and detection time (x-axis) are used as axes. The wavelength is 390 nm to 720 nm, and the time range is 45 ns with an excitation wavelength of 355 nm. The gate width of the TRiCAM  is set to 3 ns.

In summary, LIF shapes strongly rely on TGs (Figure 2) [1], thus providing a new approach for discriminating different oils. CDTRFIs are proposed to improve the identification capabilities compared with LIF and the classification speed compared with total luminescence spectroscopy. Outperforming the steady-state fluorescence approach, the proposed approach facilitates the analysis and discrimination of edible oils. The feasibility and reliability of the method are demonstrated by analyzing peanut oils of three brands in a supplementation trial. The method may be further improved by reducing the GW of the ICCD and the laser duration and by using smaller increments in the contour diagrams. Created to provide unique fingerprints for edible oils, this technology provides food security researchers with a rapid and reliable means of analyzing and classifying edible oils.


Graphs courtesy of Taotao Mu, School of Optoelectronics, Beijing Institute of Technology.


1. Mu TT, Chen SY, Zhang YC, Chen H, Guo P (2014) Characterization of edible oils using time-resolved fluorescence. Analytical Methods 6: 940-943.

Oxygen transport across slippery and curved gas-liquid interfaces using phosphorescence lifetime imaging

The transport phenomena at interfaces often determine or limit the overall performance of processes. Direct investigations of interfacial transport of momentum, mass and heat at the interfaces in micron scale are highly appreciable for further optimization of various micro and macroscale technologies. The Soft Matter Group at the University of Twente (The Netherlands), lead by Prof. Dr. Rob Lammertink, aims at gaining a better understanding of transport phenomena near boundaries, so that various processes such as desalination, separation of species and (photo)catalytic reactions can be improved.

Microfluidics offer an ideal platform allowing for the integration of 'controllable' surfaces and direct measurements of transport phenomena near them. Elif Karatay used a microfluidic bubble mattress during her PhD studies at the University of Twente, fabricating one of the microchannel walls as a superhydrophobic surface consisting of alternating solid walls and micro-bubbles (figure 1). She experimentally measured and numerically estimated the dynamic mass transfer of gas absorption at stable gas-liquid interfaces for short contacting times.

Figure 1. Microfluidic bubble mattress (a). Numerical simulations of dissolved oxygen in water with identical settings in FLIM experiments (b), the color bar shows the oxygen concentration. Lifetime field resolved by FLIM superimposed on the bright-field microscopy image showing bubbles protruding into the water (c), the color bar indicates fluorescence lifetime in nanoseconds.

Figure 1. Microfluidic bubble mattress (a). Numerical simulations of dissolved oxygen in water with identical settings in FLIM experiments (b), the color bar shows the oxygen concentration. Lifetime field resolved by FLIM superimposed on the bright-field microscopy image showing bubbles protruding into the water (c), the color bar indicates fluorescence lifetime in nanoseconds.

The rate of gas absorption into water was studied by in situ measurements of dissolved oxygen concentration profiles in aqueous solutions flowing over oxygen bubbles by frequency-domain fluorescence-lifetime imaging (FD-FLIM) microscopy.  FLIM was used to image the oxygen concentration using an oxygen sensitive luminescent dye, ruthenium tris(2,20 -dipyridyl) dichloride hexahydrate (RTDP), obeying a mono-decay function as quenched by oxygen. For the FLIM experiments, an eXtended Lambert Instruments FLIM Attachment (LIFA-X) system was used on a Zeiss Axio Observer inverted microscope. The LIFA-X, consisting of a LED light source and a Lambert Instruments intensified CCD camera (TRiCAM), was operated in gated mode to obtain phosphorescence lifetimes.

For calibration measurements, lifetimes of the RTDP were measured in oxygen-free (N2 saturated), aerated and oxygen-saturated aqueous solutions, where the micro-bubbles were established by nitrogen, air and oxygen gases, respectively. During the mass transfer experiments, oxygen gas micro-bubbles were established at the boundary of the microchannels and deoxygenated RTDP aqueous solution was the working liquid flowing past the transversely aligned oxygen bubbles (figures 1c and 2).

The lifetime of RTDP across the liquid side microchannel height was measured at different axial locations (figure 2). The bubble interface profiles and locations were experimentally determined by locating the minimum lifetime data measured near the hybrid wall. Figure 2} shows the successive lifetime fields measured with FLIM at different axial locations along the same microchannel embedded with micro-bubbles where the increasing boundary layer thickness along the downstream flow can be observed. Here, in figure 2c, the thickness of the diffusion boundary layer is 23% of the microchannel height H, and does not extend further into the microchannel due to a relatively large Reynolds number Re of 7.5.

Figure 2. Successive lifetime fields in axial position x. Quantitative visualization of the increasing boundary layer thickness along downstream flow (45 μl/min). The flow direction is from left to right. The color bar refers to the lifetime, which is given in nanoseconds. The dashed arrows indicate the axial positions at which the local oxygen concentration profiles across the microchannel height are obtained.

Figure 2. Successive lifetime fields in axial position x. Quantitative visualization of the increasing boundary layer thickness along downstream flow (45 μl/min). The flow direction is from left to right. The color bar refers to the lifetime, which is given in nanoseconds. The dashed arrows indicate the axial positions at which the local oxygen concentration profiles across the microchannel height are obtained.

The local profiles of oxygen flux from the gaseous phase to the liquid phase were obtained using the local concentration gradients measured by FLIM. Furthermore the space-averaged total flux of oxygen absorption was calculated from these local flux profiles.

The experimental results obtained by FLIM revealed an additional mass transfer resistance to gas dissolution on slippery micro-bubbles at short contact times of gas and liquid. This mass transfer resistance results in slower gas absorption than predicted by the conventionally accepted equilibrium interface model, Henry’s Law. Whereas the experimental results are in good agreement with the numerical results obtained from simulations considering non-equilibrium conditions. The results indicate that the phase equilibrium state may not be established at short contacting times.


Graphs courtesy of Dr. Elif Karatay, Stanford University, USA


E. Karatay, A.P. Tsai and R.G.H. Lammertink, Rate of gas absorption on a slippery bubble mattress, Soft Matter, 2013, 9, 11098-11106

Confocal FLIM Applications

Confocal imaging on a widefield fluorescence microscope can now be done in combination with frequency-domain fluorescence lifetime imaging microscopy (FLIM). The increased spatial resolution in the z-direction results in lifetime images with enhanced contrast as the detection of out-of-focus emission is reduced significantly. This allows you to see differences in fluorescence lifetime e.g. between the cell membrane and the cytoplasm.

The data below were obtained with the Lambert Instruments FLIM Attachment (LIFA), either widefield (with LED light; 468nm peak) or confocal (with spinning disk CSU10 and 470nm-diode laser). The fluorescence lifetime images are generated at 2 different z-positions, z1 and z2. Snapshots of several z-positions are shown, as well as movies through even more.


(spinning disk)







These images show different pollen grains: the lifetime in pseudo colours and the intensity in grey scale. Because of the pinholes in the spinning disk, the exposure time is higher with confocal imaging. In this image 220 ms exposure time per phase step was taken for the confocal image versus 195 ms for the widefield (LED light). However, when a diode laser with higher power is used, the exposure time can be shortened.


(spinning disk)






These images show YFP-transfected mammalian cells and were taken with 12 phase steps of each 400 ms for the confocal, versus 70 ms for the widefield image. The calculated lifetimes are 2.34 ns (z1) and 2.42 ns (z2) in the confocal images, versus 2.58 ns (z1) and 2.57 ns (z2) in the widefield images.

The differences in lifetime could be due to the fact that the diode laser has one excitation wavelength, exactly 470 nm, while the LED has a range of wavelengths for which we used the emission band pass filter of 465-495 nm.

Spectrally Resolved FLIM

The spectrally-resolved Lambert Instruments FLIM Attachment (LIFA) is an imaging system for fluorescence microscopy that preserves the information required to determine the position, spectrum, and lifetime of the observed fluorescence. This is done by combining several modular components. These consist of the typical LIFA (modulated intensified CCD camera, modulated LED excitation) and a prism-based imaging spectrograph.


Lifetime (pseudocolors)

Intensity (grayscale)




Spectrally resolved


2D image


The spectrally resolved images show one line (y-axis) out of the 2D intensity image of typical dicot root. The emission wavelengths at the x-axis start at 515 nm. The fluorescence lifetime is shown in pseudo colors. The higher wavelength components have a shorter lifetime (blue) than the short wavelength components (red).


Lifetime (pseudocolors)

Intensity (grayscale)


Spectrally resolved GFP transfected cells (donor only)


Spectrally resolved GFP-RFP transfected cell (FRET)

The spectrally resolved images show one line (y-axis) out of a sample of GFP transfected cells or GFP-RFP transfected cells. The emission wavelengths at the x-axis start at 515 nm; the first peak is the GFP emission peak and the second the RFP emission peak. The fluorescence lifetime is shown in pseudo colors. In the FRET sample (GFP-RFP) the fluorescence lifetime of the GFP has decreased from 2.3 ns (red) to 2.0 ns (yellow).

Total Internal Reflection Fluorescence Lifetime Imaging Microscopy

Total Internal Reflection Fluorescence (TIRF) microscopy facilitates extremely high-contrast visualization and thereby high sensitivity of fluorescence near the cover glass. Typically, the optical section adjacent to the cover glass is about 100 nm. TIRF does not disturb cellular activity, thus enabling tracking of biomolecules, and the study of their dynamic activity and interactions at the molecular level. TIRF enables the selective visualisation of processes and structures of the cell membrane and pre-membrane space like vesicle release and transport, cell adhesion, secretion, membrane protein dynamics and distribution or receptor-ligand interactions. The unique combination of TIRF and frequency domain FLIM makes it possible to measure lifetimes of, for instance, small focal adhesions near the cover glass.


Lifetime (pseudocolors)

Intensity (grayscale)





These cells (kindly provided by Ms. S.E. Le Devedec, Leiden University, The Netherlands) express dSH2-GFP in small focal adhesions as well as in the nuclei as shown by widefield microscopy. However, by the use of TIRF only fluorescence close to the coverslip is obtained, thus only the focal adhesions are excited.

Fluorescence lifetime images give a more accurate measurement in TIRF mode, as out of focus light is emitted from the average lifetime in the focal adhesions.

The images shown here are taken with the Nikon TE2000-U widefield microscope with white-TIRF illuminator, combined with the Lambert Instruments Fluorescence lifetime imaging Attachment (LIFA). As light source the modulated LED of 468nm 3W was used and as demonstrated here enough intensity was generated to obtain fluorescence lifetime images with TIRF.


Lifetime (pseudocolors)

Intensity (grayscale)








Cells (kindly provided by Ms. S.E. Le Devedec, Leiden University, The Netherlands) expressing dSH2-GFP in small focal adhesions as well as in the nuclei.

The images shown here are taken with the Olympus TIRFM (laser-TIRF), combined with the Lambert Instruments FLIM Attachment (LIFA). As light source the modulated diode laser of 473 nm 20 mW was used and as demonstrated here enough intensity was generated to obtain fluorescence lifetime images with TIRF.


Lifetime (pseudocolors)

Intensity (grayscale)




Cells (kindly provided by Ms. S.E. Le Devedec, Leiden University, The Netherlands) expressing dSH2-GFP in small focal adhesions as well as in the nuclei.

Fluorescence-lifetime imaging of synapse specific interactions in live neurons

Information processing and storage in the brain involves fast communication between neurons. In the central nervous system a neuron can be connected to up to thousands of other neurons through brain synapses, specialized junctions where the axon of one neuron meets the dendrite of another neuron (two examples are shown by red arrows on the images below). Over time, the efficacy of synaptic transmission can vary depending upon synaptic activity, a mechanism called synaptic plasticity that is largely responsible for learning and memory. During synaptic plasticity, proteins can be specifically accumulated at synapses through protein-protein interactions. The group of Daniel Choquet at the University of Bordeaux Segalen aims at gaining a better understanding of protein accumulation at synapses to unravel the molecular mechanisms underlying memory storage in the brain.

Figure 1. the donor and acceptor proteins co-localize in neurons.

Figure 1. the donor and acceptor proteins co-localize in neurons.

Using primary neuronal cultures from rat hippocampus, PhD student Anne-Sophie Hafner studied the localization of interactions between two specific neuronal proteins; an intracellular scaffolding protein and a transmembrane receptor. She transfected neurons with DNA encoding a synaptic protein coupled to Green Fluorescence Protein (GFP) (donor, green image) and a transmembrane protein coupled to mCherry (acceptor, red image). She used the Lambert Instruments FLIM Attachment (LIFA) on a confocal spinning disk to monitor GFP lifetime with or without acceptor proteins. During the experiment, cells are first imaged through the spinning disk to ensure the presence and co-localization of the donor and acceptor proteins. As shown in figure 1, both proteins are co-localized in all cell compartments, the synapse scaffolding protein being enriched in synapses. Then, the cells are imaged on the LIFA attached TRiCAM, to generate the FLIM image (lifetime color coded for each pixel, last image).

Figure 2. the donor and acceptor protiens interact specifically in synapses.

Figure 2. the donor and acceptor protiens interact specifically in synapses.

An example lifetime image of a neuron is shown in Figure 2. In the soma and dendrites, GFP lifetime is high (i.e. no interaction between the proteins of interest), and not different compared to GFP lifetime without acceptor fluorophore which stands around 2.6 nanoseconds. In contrast, GFP lifetime in synapses is dramatically decreased in presence of the acceptor protein to a mean value of 1.9 nanoseconds (i.e. the proteins of interest are interacting specifically in synapses). The Lambert Instruments FLIM technology allowed us to show and quantify for the first time the synapse specific interaction of the two proteins of interest.


Data courtesy of Anne-Sophie Hafner, Interdisciplinary Institute for Neuroscience, University of Bordeaux Segalen, France

Diffuse Optical Tomography

The TRiCAM is a gain-modulated intensified CCD camera for Near-Infrared Diffuse Optical Tomography. It allows scientific-grade imaging of tissue properties for 3D reconstruction of chromophore concentrations in biomedical optics. Its well-established frequency-domain technology allows fast acquisition of macroscopic images at high accuracy. The TRiCAM comes with a dual signal generator and power supply and optional software for extracting the phase shift and demodulation information. Lambert Instruments also offers high modulation depth laser diodes that can be modulated across a broad frequency range for optimal sensitivity.

The Lambert Instruments TRiCAM is easy to operate and has been used in optical breast cancer screening and brain imaging.

TRiCAM Key Features

  • Highly sensitive and fast diffuse optical tomography acquisition
  • Higher quantum efficiency with the optional Gen III GaAs intensifier
  • Easy integration into biomedical imaging systems

Biomolecular Interactions

Inside cells specific interactions between biomolecules are involved in almost any physiological process. Sensing extracellular signals is a matter of receptor to adapter interactions and an intricate network of structural protein interactions maintains the shape of the cell. Finding interactions between proteins involved in common cellular functions is a way to get a broader view of how they work co-operatively in a cell. One way to observe biomolecular interactions is by doing Forster Resonance Energy Transfer (FRET) measurements. In this article some examples of different interactions are given, with the link to the paper in question.

Protein-Protein Interactions

Signal transduction pathways inside cells involve the coupling of ligand-receptor interactions to many intracellular events. These events include phosphorylation by tyrosine kinases and/or serine/threonine kinases. Protein phosphorylation change enzyme activities and protein conformations. The eventual outcome is an alteration in cellular activity and changes in the program of genes expressed within the responding cells. Phosphorylation dynamics can be imaged by FRET, by labelling two proteins-, domains-, or phospho-epitopes that come in close proximity during a phosphorylation event.

Epidermal-Growth Factor Receptor (EGFR) phosphorylation with the eYFP-(acceptor)-labelled phosphotyrosine-binding domain and eCFP (donor)-tagged EGFR.Beta-secretase (BACE) phosphorylation with BACE-GFP (donor) transfected cells fixed and stained with phosphoserine-Cy3 (acceptor).

When the enzyme is labelled by one fluorophore of a FRET pair, and the substrate by the other, FRET is expected when the enzyme cleaves the substrate.

Presenilin 1 (PS1) is a critical component of the gamma-secretase complex. This complex is involved in the cleavage of several substrates, including the amyloid precursor protein (APP). By FLIM-FRET is shown that the low-density receptor-related protein (LRP) is a PS1 interactor and can compete with APP for gamma-secretase enzymatic activity.

Endosome fusion can also be imaged by FRET:

Fusion between primary endocytic vesicles and/ or sorting endosomes.

If you would like to know more about antibody fluorophores, like lifetime information of the Alexa dyes, please contact us.

Conformational Changes

When the N-terminus is tagged with the donor fluorophore and the C-terminus with the acceptor fluorophore (or vice versa), the conformational change of the macromolecule can be visualised by the occurrence of FRET. In the 'open' conformation no FRET will occur, while the 'closed' conformation will cause FRET. Different dyes bind to different regions in DNA and so FRET occurrence can give information on the condensation of DNA:

An example is the staining of nuclei with Hoechst, that binds to AT-rich regions and with 7-AAD (7-aminoactinomycin D) that binds to GC-rich regions. These stained nuclei give a non-homogenous FRET signal in total nuclei, hence an increased FRET efficiency is shown when the cell progresses from G1 to G2/M (condensed DNA formation) phase.

Oligomerization kinetics is used to reveal the composition of macromolecules, and can be observed by FRET:

Conformation of leptin receptors expressed in the cell membrane.

Lipid-protein interactions

Interactions between lipids and proteins can be visualised by FRET by incorporation of fluorescent lipids in the membrane and fluorescence-tagged peripheral membrane proteins.

Intensity-based FRET

In the intensity-based Forster Resonance Energy Transfer (FRET) method, change in emission intensities from donor and acceptor fluorophores is measured. During FRET, the amount of emitted photons (emission intensity) from the donor fluorophore decreases and the emission intensity from the acceptor fluorophore increases. The FRET efficiency is basically calculated from the ratio of emission intensities from donor and acceptor before and after FRET occurrence.

To obtain accurate FRET data by sensitized emission, three images have to be acquired:

  1. Donor excitation with donor emission,
  2. Donor excitation with acceptor emission,
  3. Acceptor excitation with acceptor emission.

The major advantage of this method over fluorescence lifetime imaging microscopy (FLIM)—which is a donor-based FRET detection—is that it can be carried out with standard wide-field or confocal fluorescence microscopes that are available in most laboratories. Moreover, it yields additional data on the acceptor population. However, quantitative sensitized emission requires significant attention for corrections and calibration, whereas FLIM-based FRET techniques are inherently quantitative from first physical principles. [Ref. Gadella TW Jr., FRET and FLIM techniques, 33, 2008]

Related Posts

FRET Efficiency

Forster Resonance Energy Transfer (FRET) efficiency \(E\) indicates the percentage of the excitation photons that contribute to FRET and is defined as:

E = 1−\frac{\tau_{DA}}{\tau_D}
where \(\tau_{DA}\) is the fluorescence lifetime of the donor in the presence of an acceptor, and \(\tau_D\) in the abscence of an acceptor. As you can see, the more FRET occurs, the more decrease in donor fluorescence lifetime.

FRET strongly depends on the distance between the donor and acceptor fluorophores (sixth-power relationship). Fluorescence lifetime of a fluorescent molecule is inversely proportional to its FRET efficiency, thus the higher the FRET efficiency the lower the fluorescence lifetime of the donor molecule will be.

The efficiency also depends on the donor-to-acceptor separation distance R with an inverse 6th order law due to the dipole-dipole coupling mechanism:

E = \frac{R^6_0}{R^6_0 + R^6}

with \(R\) being the distance between donor and acceptor pair and R0 being the Förster distance between donor and acceptor at which the FRET efficiency is 50%.

FRET efficiency in a single pixel of an image, does not give exact conclusions about the interactions between fluorophores. The entire 2D image gives a better overview of the interactions that occur. For example: in case of 50% FRET efficiency in a single pixel, it could be possible that 50% of the donor fluorophores have had 100% energy transfer to acceptor fluorophores, but it also could be possible that 100% of the donor fluorophores have had 50% energy transfer to acceptor fluorophores.

Related Posts


FLIM-FRET Experiments

Fluorescence Lifetime Imaging Microscopy Forster Resonance Energy Transfer (FLIM-FRET) has a lot of advantages over other FRET detection techniques. A major advantage is that FLIM-FRET measurements are more robust and quantitative than the FRET measurements done by, for example, sensitised emission FRET. Another advantage is that only the lifetime of the donor fluorophore has to be measured; steps to determine acceptor lifetimes are not needed. The acceptor fluorophore may therefore have an inefficient emission, or even may be a quencher, and still good quality FRET-data can be retrieved. This makes the FLIM-FRET method more versatile, faster, and easier. Furthermore, no corrections are needed for donor fluorophore emission bleed through in the acceptor emission channel.

Acceptor Photobleaching

When photobleaching the acceptor fluorophore during FRET, the non-radiative transfer of energy from the donor to the acceptor decreases. The donor fluorophore, in its turn, loses less energy and its fluorescence lifetime, with respect to FRET without a photobleached acceptor. Only when the acceptor is bleached completely, the lifetime of the donor fluorophore will be similar to the situation of no FRET occurrence (a donor-only situation).

Enhanced Acceptor Fluorescence (EAF)

In the case where the donor and acceptor fluorophores are both excited with the same excitation light wavelength, e.g. in the FRET pair GFP-YFP, a special kind of FRET can be detected. Namely, the average lifetime that is calculated is the contribution of both donor and acceptor fluorophores. Taking GFP and YFP as an example, GFP has a small lifetime compared to YFP. When no FRET is occurring, the average lifetime is measured of both GFP and YFP that are both excited by the 480nm wavelength light source. However, when FRET occurs, the energy of the GFP proteins transfers non-radiatively to the YFP proteins, so relatively more YFP emission (with a long lifetime) is taken into account. So, the average lifetime increases instead of decreases, as is normally the case when you measure only the lifetime of the donor fluorophore.

Related Posts


Forster Resonance Energy Transfer

Forster Resonance Energy Transfer (FRET) is the non-radiative transfer of energy from a molecule in the excited state (donor) to a molecule in the ground state (acceptor). A fluorescent donor molecule can return to the ground state by losing its energy through emission of a photon (fluorescence), or by transferring its energy to a nearby (1 - 9nm) acceptor molecule (FRET). Compared to a molecule that exhibits no FRET, the donor has more options to lose its energy. Therefore, it returns faster to the ground state, which decreases its lifetime.

FRET is a useful tool to quantify molecular dynamics like interactions of two fluorophores by microscopy. The proteins under investigation are labelled with donor fluorophores or acceptor fluorophores. Interaction between the two fluorophores is accompanied by direct energy transfer from donor to acceptor (FRET). When FRET occurs, it means that the two proteins of interest are in such close proximity that they can interact with each other.

During FRET, a quantum of energy is transferred from a donor fluorophore to an acceptor fluorophore in a nonradiative process. So, in case of no FRET, the donor fluorophore is excited and emits photons. The acceptor fluorophore does not emit photons, because it is not excited. In case of FRET, the donor fluorophore is excited, but in stead of emitting all its energy as photons, it transfers some of its energy to the acceptor fluorophore that becomes excited and emits light.

Summarising, in case of no FRET only the donor fluorophore emits photons, and in case of FRET both donor and acceptor emit photons.

FRET only occurs if...

  1. The donor fluorescence emission spectrum overlaps with the acceptor absorbance.
  2. The donor and acceptor fluorophores are in close proximity (i.e. 1 - 9nm, which is at the scale of protein size).
  3. The transition dipole moments of the donor and acceptor fluorophores are not perpendicular.

FRET pairs

To let FRET occur, the emission spectrum of the donor fluorophore has to overlap the excitation spectrum (absorbance) of the acceptor fluorophore. Some examples are BFP-YFP, CFP-YFP, GFP-DsRed, GFP-Cy3, GFP-mOrange, YFP-RFP, and Cy3-Cy5.

Browser based calculator to find the critical distance and FRET efficiency with known spectral overlap.

Related Posts

Probing the Refractive Index of the Microenvironment

Fluorescence lifetime is a property which is almost completely insensitive to fluorophore concentration. It provides the means of discrimination among molecules with a spectrally overlapped emission. A further important feature is the dependence of the fluorescence decay time to the microenvironment. This dependence varies between fluorophores and certain factors.

The fluorescence lifetime of e.g. GFP can be used to probe the direct local environment of the fluorophore, because the local refractive index affects fluorescence decay. The inverse GFP fluorescence lifetime scales approximately with the square of the refractive index.

Cell membranes normally have a higher refractive index than the cytoplasm, namely 1.46 - 1.60 and 1.35 respectively. From fluorescence lifetime measurements of GFP in a PBS solution with increasing glycerol concentrations, the expected lifetime of GFP differs from 2.17 ns in the cell membrane to 2.67 ns in the cytoplasm.

Reference: Klaus Suhling, Jan Siegel, David Phillips, Paul M. W. French, Sandrine Leveque-Fort, Stephen E. D. Webb, and Daniel M. Davis. "Imaging the environment of green fluorescent protein". Biophysical Journal, 83:3589-3595 (2002).

There was, however, no correlation observed between GFP fluorescence lifetime and the viscosity of the surrounding solution. This was researched with a variety of solutes added to GFP in buffer.

Reference: Suhling, K., D. M. Davis, and D. Phillips. "The influence of solvent viscosity on the fluorescence decay and time-resolved anisotropy of green fluorescent protein". J. Fluoresc. 12:91–95 (2002).

Ion Imaging

For ion imaging, several fluorescent indicators (sensor, construct, tracer, etc) are available that have a change in quantum yield upon ion binding. This means that they emit photons with different energy, thus have different emission wavelength. Their fluorescence lifetime could also change. Therefore, there are two methods in which ion imaging can be done by use of indicators: the ratiometric method and the FLIM method.

Another method ion imaging is by the use of Forster Resonance Energy Transfer based (FRET-based) indicators that change their conformation upon ion binding. Upon the conformational change of a FRET-based indicator, its FRET efficiency changes, which is used as indicator of ion concentration. Examples of these indicators are cameleons. Cameleons are genetically-encoded fluorescent indicators for Ca2+ based on green fluorescent protein variants and calmodulin (CaM).

Reference: Miyawaki A, Griesbeck O, Heim R, Tsien RY. "Dynamic and quantitative Ca2+ measurements using improved cameleons". Proc Natl Acad Sci USA (PNAS) 96(5):2135-40 (1999)

Demonstration of the Lambert Instruments Toggel camera for single-image FLIM (siFLIM) detection of histamine-induced alterations in Ca2+ concentration. Tiny oscillations in Ca2+ levels (~2.5 s periods) are observed after addition of histamine. Such small and rapid transients would go completely unnoticed when recorded by conventional FLIM. Video courtesy of the Netherlands Cancer Institute.

Demonstration of the Lambert Instruments Toggel camera for single-image FLIM (siFLIM) detection of histamine-induced alterations in Ca2+ concentration. Tiny oscillations in Ca2+ levels (~2.5 s periods) are observed after addition of histamine. Such small and rapid transients would go completely unnoticed when recorded by conventional FLIM.

Video courtesy of the Netherlands Cancer Institute.

Calcium Imaging

Calcium (Ca2+) is important for signal transduction pathways.

Proton (pH) Imaging

The intracellular proton (H+) concentration (pH), as well as intracellular calcium, is important in the regulation of cellular functions including growth, differentiation, motility, exocytosis and endocytosis. To study this in more detail, measurements of the intracellular pH of resting cells can be done and the pH fluctuations inside cells after environmental perturbations can be followed.

Reference: Hai-Jui Lin, Petr Herman, and Joseph R. Lakowicz. "Fluorescence Lifetime-Resolved pH Imaging of Living Cells". Cytometry Part A 52A:77–89 (2003).

Zinc Imaging

Zinc (Zn2+) is involved in enzyme catalysis, protein structure, protein-protein interactions, and protein-oligonucleotide interactions. Zinc interacts with extracellular binding sites, which are likely to include binding sites involved in the subsequent translocation of this ion to the cell interior. Inside the cell, Zinc binds to cytosolic and organelle binding sites or is taken up by intracellular organelles.

Sodium Imaging

Sodium (Na+) is important in the signal transduction in the central nerve system.

Magnesium Imaging

Many enzymes (like kinases) require the presence of magnesium Mg2+ ions for their catalytic action, especially enzymes utilising ATP.

Chloride Imaging

Chloride (Cl-) plays a role in the central nervous system.


Potassium (K+) plays a role in cell growth and cell viability.

Indicators for Ion Imaging by FLIM

  • BCECF (pH)
  • Bis-BTC (heavy metals)
  • Calcium-crimson (Calcium, orange excitation)
  • Calcium-green (Calcium, blue excitation)
  • Carboxyfluorescein (pH)
  • Carboxy-SNAFL-1 (pH)
  • Carboxy-SNAFL-2 (cytosol pH)
  • DM-NERF dextrans (lysosoml pH)
  • Fluo-3 (Calcium)
  • Fura-2 (Calcium)
  • LysoSensor DND-160 (lysosomal pH)
  • LysoSensor probe (pH)
  • Magnesium-green (Magnesium)
  • Mag-quin-1 (Magnesium)
  • Mag-quin-2 (Magnesium)
  • MQAE (Chloride)
  • Newport Green DCF (Zinc)
  • OG-514 carboxylic acid dextrans (lysosoml pH)
  • PBFI (Potassium)
  • Quin-2 (Calcium, blue excitation)
  • SPQ (Chloride)


Cell biology is the discipline that studies cells to answer scientific questions. All organisms are composed of one or more cells and all vital functions of an organism occur within cells. Cells contain the hereditary information necessary for regulating cell functions. Cells possess DNA, the hereditary material of genes, and RNA, containing the information necessary to build various proteins such as enzymes, the cell's primary machinery. There are also other kinds of biomolecules in cells, e.g. lipids, proteins, macromolecules, and more.

Cell biology research includes learning the physiological properties such as the structure and the organelles of cells, their environment, interactions, life cycle, division, function, and eventual death. This is done both on microscopic and molecular level, and includes the research of single-celled organisms like bacteria as well as specialized cells in multi-cellular organisms like humans.

Knowing the composition of cells and how cells work is fundamental to all of the biological sciences. Appreciating the similarities and differences between cell types is particularly important to the fields of cell and molecular biology. These fundamental similarities and differences provide a unifying theme, allowing the principles learned from studying one cell type to be extrapolated and generalized to other cell types.

In combination with our products, wide field fluorescence microscopy is used to measure characteristics of fluorescent proteins. Cells, originating from bacteria and insects to mammals, generally are kept in culture and plated at coverslips to do specific experiments. Microscopy enables viewing objects inside cells that are stained or fluorescently tagged. By observing the characteristics, e.g. the fluorescence lifetime, of the fluorescent compounds, not just the localization of a fluorescent protein, but also the characteristics of its local environment can be imaged. Novel multi-parameter fluorescence imaging systems are being used to study intracellular organization and inter- and intracellular signalling.

One way to observe the proteins, is by fixation of the cells to the coverslips. Before the cells are fixed, the compounds in the cells can be fluorescently tagged (see living cell-imaging). Also, the compounds inside the cells can be stained after their fixation, for example by use of antibodies. Staining is a biochemical technique of adding a class-specific (DNA, proteins, lipids or carbohydrates) dye to a substrate to qualify or quantify the presence of a specific compound. The characteristics of the dyes can give answers to specific scientific questions, like whether there is interaction between two different proteins, whether there is a conformational change of the protein after a kind of treatment (see also Fluorescence Lifetime Imaging Microscopy and Forster Resonance Energy Transfer), or whether specific ions have bound to the proteins of interest, etc.

The cells can also be analyzed in-vivo. These living cell imaging experiments seek to gain information about the localization and interaction of the desired protein. One way to do this is to replace the wild-type gene with a 'fusion' gene that has a reporting element such as GFP. That will allow easy visualization of the products of the genetic modification. More sophisticated techniques are in development that can track protein products without mitigating their function, such as the addition of small sequences which will serve as binding motifs to monoclonal antibodies.

Medical Diagnosis

Biological tissues show intrinsic fluorescence lifetime imaging microscopy contrast because of the presence of autofluorescent structures like riboflavins and NADH. While autofluorescence is disadvantageous for observing tagged proteins of interest with e.g. fluorescence lifetime imaging microscopy, the exploitation of differences in the autofluorescent properties of biological tissue will increase the throughput and reliability of histopathological screening.

Autofluorescence can be used to detect molecular changes arising from diseases such as cancer and to differentiate normal cells/tissues from cancerous cells/ tissues. Namely, between normal and tumour cells differences in lifetime of normal autofluorescence have been detected. One approach is the combination of fluorescence lifetime imaging microscopy to endoscopy, whereby the endoscope is equipped with a laser. During endoscopic research, the tumour cells can be recognised and immediately burned away with the high-power laser.

Related Posts